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Providing Informative Feedback in a Low-Cost Rehabilitation System Using Machine Learning

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Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15347))

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Abstract

Rehabilitation is a core process in helping people recover from a wide range of health issues, including injuries and diseases. Although advancements in technology and the use of artificial intelligence have facilitated the development of tools to aid in rehabilitation processes, there is a lack of low-cost solutions that patients, without requiring advanced care, can use at home. In this work, we propose a low-cost intelligent system for lower limb rehabilitation that uses machine learning to provide informative feedback to users. Compared to existing solutions, our system offers the advantage of real-time feedback, informing patients whether they are performing exercises correctly. It also suggests posture corrections to prevent injuries and accelerate the recovery process. Moreover, our system can be used at home on a smartphone, tablet, or personal computer, and does not require patients to purchase additional devices, which is a significant benefit. The system includes four exercises: Squat, Romanian Deadlift, Glute Bridge, and Donkey Kick. Validation tests with end-users reinforced the usability of this system and confirmed the importance of real-time feedback. The results were also useful for identifying areas for improvement, particularly with the Squat exercise, which is among the more challenging exercises to perform correctly.

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Notes

  1. 1.

    https://biodexrehab.com/products/system-4-pro/.

  2. 2.

    https://www.ess.ipp.pt/.

  3. 3.

    https://www.zerozero.pt/equipa/melgacense/6852.

  4. 4.

    https://www.kaggle.com/datasets/hasyimabdillah/workoutexercises-images?select=barbell+biceps+curl.

  5. 5.

    https://www.youtube.com/.

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Acknowledgments

This work was partially supported by the UIDB/05105/2020 Program Contract, funded by national funds through the FCT I.P.

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Authors and Affiliations

  1. FEUP – Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Paul Rodrigues

  2. PORTIC – Porto Research, Technology and Innovation Center, Polytechnic of Porto (IPP), 4200-374, Porto, Portugal

    Ivone Amorim

  3. Research on Economics, Management and Information Technologies, REMIT, Portucalense University, Rua Dr. António Bernardino Almeida, 541-619, 4200-072, Porto, Portugal

    Bruno Cunha

Authors
  1. Paul Rodrigues
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  2. Ivone Amorim
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  3. Bruno Cunha
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Corresponding author

Correspondence to Ivone Amorim .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

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Rodrigues, P., Amorim, I., Cunha, B. (2025). Providing Informative Feedback in a Low-Cost Rehabilitation System Using Machine Learning. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-77738-7_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-77738-7

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